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All Journal Jurnal Pendidikan Teknologi dan Kejuruan EMITTER International Journal of Engineering Technology The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Setiawardhana
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Journal : EMITTER International Journal of Engineering Technology

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Implementation of Portable Ultrasound for Heart Disease Detection Using Cloud Computing-Based Machine Learning Sigit, Riyanto; Rika Rokhana; Setiawardhana; Taufiq Hidayat; Anwar; Jovan Josafat Jaenputra
EMITTER International Journal of Engineering Technology Vol 12 No 2 (2024)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v12i2.904

Abstract

Heart disease remains one of the leading causes of death globally, including in Indonesia. Cardiovascular disease is the leading cause of death worldwide, resulting in a significant number of fatalities. In Indonesia, access to specialized heart examination services is limited, requiring patients to visit large hospitals equipped with specialized facilities. Echocardiographic examinations using ultrasound can measure various heart parameters, such as hemodynamics, heart mass, and myocardial deformation. Portable ultrasound devices have emerged, enabling flexible and effective heart examinations. These devices capture video data of the patient's heart condition. The data undergoes image preprocessing involving median filtering, high-boost filtering, morphological operations, thresholding, and Canny filtering. Segmentation is performed using region filters, collinear filters, and triangle equations. Tracking utilizes the Optical Flow Lucas-Kanade method, and feature extraction employs Euclidean distance and trigonometric equations. The classification stage uses Support Vector Machine (SVM). Video data is transmitted via a mobile application to the cloud, where all stages from preprocessing to classification are conducted on cloud servers. The classification results are then sent back to the mobile application. The proposed model achieved an accuracy rate of 86% with a standard deviation of 0.09, indicating that the detection system performs effectively.
The Impact of Social Force Model Parameters On Frontier-Based Exploration Performance Irsyad, Asyam; Dewantara, Bima Sena Bayu Dewantara; Setiawardhana
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.994

Abstract

Autonomous exploration is one of the most challenging tasks in mobile robotics, particularly in environments that contain dynamic obstacles and require fully autonomous mapping without human intervention. This study addresses the dual problem of enabling navigation in the presence of potential static obstacles and achieving autonomous map building. To solve this, we utilize the Social Force Model (SFM), which offers a behavior-based approach suitable for dynamic and uncertain environments. The objective of this research is to investigate how different SFM parameters—Gain (ks), Radius (rR), and Effective range (ψs)—influence the effectiveness of autonomous exploration. Experiments were conducted using a TurtleBot3 robot in a simulated 155 m² environment, where various parameter combinations were tested. Evaluation metrics included mapping completion, failure types, travel distance, and exploration duration. Results indicate that tuning the SFM parameters significantly affects the robot's ability to explore autonomously and avoid obstacles. Extremely low parameter values led to collisions, while excessively high values caused unstable or inefficient behavior. The Radius parameter had a major impact on spatial awareness, and moderate effective range values contributed to stable tracking. Furthermore, higher frontier sensing latency resulted in longer exploration times. This study provides practical insights into the sensitivity of SFM parameters and offers guidance for optimizing navigation systems for fully autonomous exploration in both simulated and real-world settings.
Co-Authors Agus Indra Gunawan Amadea Permana Sanusi Anwar Arisdiawan, Rossi Armanto, Ony Arna Fariza Bima Sena Bayu Dewantara Catoer Ryando Dewantara, Bima Sena Bayu Dewantara Irsyad, Asyam Jovan Josafat Jaenputra Kadek Reda Setiawan Suda M Udin Harun Al Rasyid, M Udin Harun Muhtar, Muhdalifah Novie Ayub Windarko Rika Rokhana Riyanto Sigit, Riyanto Sanusi, Muhammad Anwar Sigit Riyanto Taufiq Hidayat Wahyu, Zaky Oktavianto